The field of the invention is coherent imaging using vibratory energy, such as ultrasound and, in particular, methods for increasing the quality of ultrasound images.
There are a number of modes in which ultrasound can be used to produce images of objects. The ultrasound transmitter may be placed on one side of the object and sound transmitted through the object to the ultrasound receiver placed on the other side (“transmission mode”). More commonly, the receiver is positioned on the same side of the object as the transmitter, or the transmitter and receiver use the same transducer, and an image may be produced in which the brightness of each pixel is a function of the amplitude of the ultrasound reflected from the object back to the receiver (“backscatter” or “echo” mode). The present invention relates to a backscatter method for producing ultrasound images.
There are a number of well known backscatter methods for acquiring ultrasound data. In the so-called “A-scan” method, an ultrasound pulse is directed into the object by the transducer and the amplitude of the reflected sound is recorded over a period of time. The amplitude of the echo signal is proportional to the scattering strength of the reflectors in the beam path and the time delay is proportional to the range of the reflectors from the transducer. In the so-called “B-scan” method, the transducer transmits a series of ultrasonic pulses as its focused beam is scanned across the object along a single axis of motion. The resulting echo signals are recorded as with the A-scan method and their amplitudes are used to modulate the brightness of pixels on a display. With the B-scan method, enough data are acquired from which a two-dimensional image of the reflectors can be reconstructed. Two-dimensional real time imaging is achieved by repeating the B-scan at up to 100 image frames per second to depict moving subjects such as the beating heart.
The data acquisition rate in ultrasound is limited by pulse-echo propagation delays in tissue. Because the speed of sound in soft tissue is around 1540 m/s (0.154 cm/μs), the maximum pulse repetition frequency (PRF) that can be utilized for imaging soft tissue is 77,000/D, where D is the image depth in centimeters. When this allowable PRF is distributed over 200 beam lines/image, and the image depth is 10 cm, the maximum 2D image rate is 38.5/s. However, when acquiring a volume of data containing 50 or more such image planes, the maximum 3D acquisition rate falls to about one volume per second, which is too low for quantifying velocities in flowing blood. Methods being investigated for more rapid volume acquisitions include transmitting broad beams followed by parallel processing of narrow received beam lines. Also being investigated are methods in which total volume plane wave insonification is utilized along with storage of vast amounts of un-beamformed data from individual channels and subsequent synthetic aperture processing. In addition to the vast data density requirements of these approaches, when beamforming is restricted to the receive mode only the possibilities for strong side lobes is introduced, which could be restrictive particularly for detecting weak echoes from flowing blood.
In Doppler ultrasound imaging, the reflected echo signals are processed and the relative motion between the reflecting structures and the ultrasonic transducer measured. In these methods, the focused ultrasonic beam is scanned across the object in a manner similar to a B-scan, but multiple echoes are acquired from each refractor in the object in order to acquire sufficient information to measure its relative velocity.
Doppler ultrasound may, for example, provide blood velocity information at a series of phases during the cardiac cycle. Doppler ultrasound processing to form color flow images requires multiple transmit-receive pulse sequences along each sampled beamline to acquire the flow information. For conventional single plane “color flow” imaging, to achieve the desired high frame rates it is currently necessary to reduce the ultrasound beam line density, which results in a sparser coverage of the imaged region with interrogating acoustic beam lines. For emerging 3D and 4D flow imaging, the data acquisition times are further challenged, forcing tradeoffs between volume data acquisition rates, velocity accuracy, and spatial resolution.
While many real time ultrasound imaging clinical applications require that each image frame be acquired with less than optimal sampled data, under sampling is not the only problem. The carrier frequency of the ultrasound beam presents another tradeoff between image resolution and image signal-to-noise ratio (SNR). Image resolution may be increased by increasing the carrier frequency (e.g., from 4 MHz to 25 MHz); however, higher ultrasound frequencies do not penetrate tissues as well as lower frequencies, and as a result, at any given depth below the tissue surface the SNR of the image will be reduced. In practice this means that lower image resolution is required at deeper depths within the subject in order to maintain the image at a clinically useful quality.
Ultrasonic transducers for medical applications are constructed from one or more piezoelectric elements sandwiched between a pair of electrodes. Such piezoelectric elements are typically constructed of lead zirconate titanate (PZT), polyvinylidene diflouride (PVDF), or PZT ceramic/polymer composite. The electrodes are connected to a voltage source, and when a voltage is applied, the piezoelectric elements change in size at a frequency corresponding to that of the applied voltage. When a voltage pulse is applied, the piezoelectric element emits an ultrasonic wave into the media to which it is coupled at the frequencies contained in the excitation pulse. Conversely, when an ultrasonic wave strikes the piezoelectric element, the element produces a corresponding voltage across its electrodes. Typically, the front of the element is covered with an acoustic matching layer that improves the coupling with the media in which the ultrasonic waves propagate. In addition, a backing material is disposed to the rear of the piezoelectric element to absorb ultrasonic waves that emerge from the back side of the element so that they do not interfere. A number of such ultrasonic transducer constructions are disclosed in U.S. Pat. Nos. 4,217,684; 4,425,525; 4,441,503; 4,470,305 and 4,569,231.
When used for ultrasound imaging, the transducer typically has a number of piezoelectric elements arranged in an array and driven with separate voltages (apodizing). By controlling the time delay (or phase) and amplitude of the applied voltages, the ultrasonic waves produced by the piezoelectric elements (transmission mode) combine to produce a net ultrasonic wave focused at a selected point. By controlling the time delay and amplitude of the applied voltages, this focal point can be moved in a plane to scan the subject.
The same principles apply when the transducer is employed to receive the reflected sound (receiver mode). That is, the voltages produced at the transducer elements in the array are summed together such that the net signal is indicative of the sound reflected from a single focal point in the subject. As with the transmission mode, this focused reception of the ultrasonic energy is achieved by imparting separate time delay (and/or phase shifts) and gains to the signal from each transducer array element.
This form of ultrasonic imaging includes phased array sector scanning, linear array scanning and curvilinear array scanning. Such a scan includes a series of measurements in which the steered ultrasonic wave is transmitted, the system switches to receive mode after a short time interval, and the reflected ultrasonic wave is received and stored. Typically, the transmission and reception are steered along the same beam path during each measurement to acquire data from a series of points along an acoustic scan line. The receiver is dynamically focused at a succession of ranges (R) along the scan line as the reflected ultrasonic waves are received. The time required to conduct the entire scan is a function of the time required to make each measurement and the number of measurements required to cover the entire region of interest at the desired resolution and signal-to-noise ratio. For example, a total of 128 scan lines may be acquired over a 90 degree sector, with each scan line being steered in increments of 0.70 degrees. A number of such ultrasonic imaging systems are disclosed in U.S. Pat. Nos. 4,155,258; 4,155,260; 4,154,113; 4,155,259; 4,180,790; 4,470,303; 4,662,223; 4,669,314 and 4,809,184.
Recently a new image reconstruction method known in the art as highly constrained backprojection, or “HYPR”, and described in co-pending U.S. patent application Ser. No. 11/482,372, filed on Jul. 7, 2006 and entitled “Highly Constrained Image Reconstruction Method” was disclosed, and is herein incorporated by reference. With the HYPR method a composite image is reconstructed from acquired data to provide a priori knowledge of the subject being imaged. This composite image is then used to highly constrain the image reconstruction process. HYPR has been used in a number of different imaging modalities including magnetic resonance imaging (MRI), x-ray computed tomography (CT), positron emission tomography (PET), single photon emission computed tomography (SPECT), and digital tomosynthesis (DTS).
As shown in FIG. 1, for example, when a series of time-resolved images 102 are acquired in a dynamic study, each image frame 102 may be reconstructed using a very limited set of acquired views. However, each such set of views is interleaved with the views acquired for other image frames 102, and after a number of image frames have been acquired, a sufficient number of different views are available to reconstruct a quality composite image 103 for use according to the HYPR method. A composite image 103 formed by using all the interleaved projections is thus much higher quality, and this higher quality is conveyed to the image frame by using the highly constrained image reconstruction method 104.
A discovery of the HYPR method is that good quality images can be produced with far fewer projection signal profiles if a priori knowledge of the signal contour in the FOV 212 is used in the reconstruction process. Referring to FIG. 2, for example, the signal contour in the FOV 212 may be known to include structures such as blood vessels 218 and 220. That being the case, when the backprojection path 208 passes through these structures a more accurate distribution of the signal sample 214 in each pixel is achieved by weighting the distribution as a function of the known signal contour at that pixel location. As a result, a majority of the signal sample 214 will be distributed in the example of FIG. 2 at the backprojection pixels that intersect the structures 218 and 220. For a backprojection path 208 having N pixels this highly constrained backprojection may be expressed as follows:
                              S          n                =                              (                          P              ×                              C                n                                      )                                              ∑                              n                =                1                            N                        ⁢                          C              n                                                          (        1        )            
where: Sn is the backprojected signal magnitude at a pixel n in an image frame being reconstructed, P is the signal sample value in the projection profile being backprojected, and Cn is the signal value of an a priori composite image at the nth pixel along the backprojection path. The composite image is reconstructed from data acquired during the scan, and may include that used to reconstruct the image frame as well as other acquired image data that depicts the structure in the field of view. The numerator in equation (1) weights each pixel using the corresponding signal value in the composite image and the denominator normalizes the value so that all backprojected signal samples reflect the projection sums for the image frame and are not multiplied by the sum of the composite image.
While the normalization can be performed on each pixel separately after the backprojection, in many clinical applications it is far easier to normalize the projection, P, before the backprojection. In this case, the projection, P, is normalized by dividing by the corresponding value, PC, in a projection through the composite image at the same view angle. The normalized projection, P/PC, is then backprojected and the resulting image is then multiplied by the composite image.
A 3D embodiment of the highly constrained backprojection is shown pictorially in FIG. 3 for a single 3D projection view characterized by the view angles, θ and φ. This projection view is back projected along axis 216 and spread into a Radon plane 221 at a distance, r, along the back projection axis 216. Instead of a filtered back projection in which projection signal values are filtered and uniformly distributed into the successive Radon planes, along axis 216, the projection signal values are distributed in the Radon plane 221 using the information in the composite image. The composite image in the example of FIG. 3 contains vessels 218 and 220. The weighted signal contour value is deposited at image location, (x,y,z), in the Radon plane 221 based on the intensity at the corresponding location, (x,y,z), in the composite image. This is a simple multiplication of the backprojected signal profile value, P, by the corresponding composite image voxel value. This product is then normalized by dividing the product by the projection profile value from the corresponding image space projection profile formed from the composite image. The formula for the 3D reconstruction is:
                              I          ⁡                      (                          x              ,              y              ,              z                        )                          =                  ∑                      (                                                            P                  ⁡                                      (                                          r                      ,                      θ                      ,                      ϕ                                        )                                                  *                                                      C                    ⁡                                          (                                              x                        ,                        y                        ,                        z                                            )                                                                            (                                          r                      ,                      θ                      ,                      ϕ                                        )                                                                                                P                  C                                ⁡                                  (                                      r                    ,                    θ                    ,                    ϕ                                    )                                                      )                                              (        2        )            
where the sum, Σ, is over all projections in the image frame being reconstructed and the (x,y,z) values in a particular Radon plane are calculated using the projection profile value, P(r,θ,φ), at the appropriate (r,θ,φ) value for that plane. PC(r,θ,φ) is the corresponding projection profile value from the composite image, and C(x,y,z)(r,θ,φ) is the composite image value at (r,θ,φ).
The HYPR image reconstruction method has been used primarily to reduce image artifacts due to undersampling in MRI and x-ray CT. However, HYPR can also be used to improve the SNR of an image. For example, the image frames 102 may be acquired in a dynamic study in which the dosage (e.g., x-ray) or exposure time (e.g., PET or SPECT) is reduced for each image frame. In this case the composite image is formed by accumulating or averaging measurements from the series of acquired low SNR image frames 102 to produce a higher SNR composite image 102. The highly constrained image 104 produced from each image frame 102 takes on the higher SNR of this composite image.